SKILLEMALL.ai

AC zmm-concentration

📐 詹明明·大客户会不会跑 ——客户与收入集中度体检。你的钱有多少是「一个人不高兴就没了」的:几个大客户占多少、一个渠道带来多少、一个产品撑了多少。算出「失去最大那个之后还能撑几个月」,给出风险清单和禁止动作清单。 触发方式:/zmm-concentration、/大客户风险、/集中度、「大客户会不会跑」「一个客户占太多」「渠道太单一」「客户结构健不健康」「他要是走了怎么办」 Revenue concentration checkup for owner-operators: how much of your money hangs on one customer, one channel, or one product — plus months of runway if the biggest one leaves. Outputs a risk list and a do-not-touch list. Trigger: /zmm-concentration, "what if my biggest client leaves", "too dependent on one customer", "is my customer mix healthy" —— 📐 詹明明 · 不给公式,给判据。每条规则都标了实测代价。

ClawHub Agent Skills author: 詹明明 v0.2.4 MIT-0 3 files body ≈ 1 156 tokens Open the sourceclawhub.ai analyzed 3 d ago

📐 詹明明·大客户会不会跑 ——客户与收入集中度体检。你的钱有多少是「一个人不高兴就没了」的:几个大客户占多少、一个渠道带来多少、一个产品撑了多少。算出「失去最大那个之后还能撑几个月」,给出风险清单和禁止动作清单。…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 34 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1156 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 572: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    The skill fits its stated business-analysis purpose, but it broadly reads shared memory and stores sensitive customer-risk observations without clear limits or user controls.
    LLM: suspicious (medium) · 6 Sept 2026